About this course: In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Taught by: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Taught by: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Basic Info | Course 2 of 5 in the Recommender Systems Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Course Introduction
- Reading: Course Structure Outline
- Video: User-User Collaborative Filtering
- Video: Configuring User-User Collaborative Filtering
- Video: Influence Limiting and Attack Resistance; Interview with Paul Resnick
- Video: Trust-Based Recommendation; Interview with Jen Golbeck
- Video: Impact of Bad Ratings; Interview with Dan Cosley
- Video: Assignment Introduction
- Reading: Assignment Instructions: User-User CF
- Reading: Introducing User-User CF Programming Assignment
- Video: Programming Assignment - Programming User-User Collaborative Filtering
- Video: Introduction to Item-Item Collaborative Filtering
- Video: Item-Item Algorithm
- Video: Item-Item on Unary Data
- Video: Item-Item Hybrids and Extensions
- Video: Strengths and Weaknesses of Item-Item Collaborative Filtering
- Video: Interview with Brad Miller
- Video: Item-Based CF Assignment Intro Video
- Reading: Item-Based CF Assignment Instructions
- Reading: Introducing Item-Item CF Programming Assignment
- Video: Programming Assignment - Programming Item-Item Collaborative Filtering
- Video: The Cold Start Problem
- Video: Recommending for Groups: Interview with Anthony Jameson
- Video: Threat Models
- Video: Explanations
- Video: Explanations, Part II: Interview with Nava Tintarev
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Purchase Course | Audit | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Available | Not available |
| Receive a final grade | Available | Not available |
| Earn a shareable Course Certificate | Available | Not available |
Great course, nice theory and interesting exercise with the sheets and making actual Java programs to implement the algorithms. I would love to see some more in-depth probability theory, and considerations about when the algorithms deviate from the theory, or connections to other theories, but I suppose the course is more accessible and interesting like this. The interviews are probably my favorite part!
Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!
Loved it...many thanks Prof. Joe for bringing this content to Coursera
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in-depth and well-made to follow